发表机构
Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非平稳EEG伪迹去除，提出基于指数移动平均子空间跟踪的自适应ASR框架，实现双时间尺度适应，在10名受试者上显著提升眨眼伪迹抑制（53.1%对比24.0%和26.7%），同时权衡信号保留。
AI 中文摘要
脑电图（EEG）伪迹去除对于现实世界脑机接口部署仍是一个关键挑战，原因在于非平稳信号统计特性和有限的校准可用性。伪迹子空间重建（ASR）提供了一种基于方差的自动伪迹抑制框架，但其依赖于静态校准导出的协方差模型，限制了在受试者内部漂移下的稳定性，并增加了对参数误设的敏感性。本工作提出了一种基于指数移动平均（EMA）子空间跟踪的自适应ASR框架。通过将短时窗协方差估计与缓慢的递归同化相结合，该方法建立了双适应时间尺度，既能响应不断演变的EEG结构，又能保持对瞬态伪迹的稳定性。该公式进一步促进了跨拒绝阈值的更平滑运行特性，降低了连续部署场景中对超参数选择的敏感性。在来自10名受试者的24通道EEG记录上进行了评估，这些记录包含不同认知状态下的显著眨眼伪迹，EMA-ASR实现了比原始ASR和有限记忆ASR变体更强的伪迹衰减（眨眼减少53.1%对比24.0%和26.7%），但以增加重建偏差和频谱偏差为代价，而有限记忆变体的表现与原始ASR相当。这些发现将基于EMA的自适应跟踪定位为一种轻量级、面向部署的方法，用于非平稳条件下的连续EEG伪迹去除，并在抑制强度与信号保留之间做出明确权衡。
英文摘要
Electroencephalogram (EEG) artifact removal remains a critical challenge for real-world brain-computer interface deployment due to non-stationary signal statistics and limited calibration availability. Artifact Subspace Reconstruction (ASR) provides an automated framework for variance-based artifact suppression but relies on a static calibration-derived covariance model, limiting stability under intra-subject drift and increasing sensitivity to parameter mis-specification. This work proposes an adaptive ASR framework based on exponential moving average (EMA) subspace tracking. By integrating short-horizon covariance estimation with slow recursive assimilation, the method establishes dual adaptation timescales that enable responsiveness to evolving EEG structure while preserving stability against transient artifacts. This formulation further promotes smoother operating characteristics across rejection thresholds, reducing sensitivity to hyperparameter selection in continuous deployment settings. Evaluated on 24-channel EEG recordings from 10 subjects with prominent blink artifacts across cognitive states, EMA-ASR achieved substantially stronger artifact attenuation than Original and memory-limited ASR variants (53.1% vs. 24.0% and 26.7% blink reduction), at the cost of increased reconstruction and spectral deviation, whereas the memory-limited variant performed comparably to Original ASR. These findings position EMA-based adaptive tracking as a lightweight, deployment-oriented approach to continuous EEG artifact removal under non-stationary conditions, with an explicit trade-off between suppression strength and signal preservation.
Comments6 pages, 2 figures, 2 tables. Code: https://github.com/NeuralLabIITGuwahati/EMA-ASR
